The scientific method relies on testing hypotheses, but what happens when a model succeeds? This article explores how organizations often fail to retest underlying assumptions, even as reality shifts. Success can become a constraint, blinding us to changing conditions. Kodak represents the classic failure to adapt beyond proven logic.

Moody's presents a subtler danger: constant change that remains bounded by existing assumptions. The piece argues that past success provides a powerful reason to maintain trust in original assumptions, yet populations and technologies evolve. When models and methods adapt without re-evaluating the problem definition, systems can drift from reality.

The core question posed is how often we should retest assumptions that still appear to work. The discussion moves from data science methods to organizational behavior, warning that continuous adaptation without fundamental re-evaluation risks embedding outdated logic into new processes. Ultimately, the article challenges readers to consider whether their success is built on valid premises or simply resistant change.